I was following the textbook by David Mackay: Information theory inference and learning algorithms.

I have question on asymptotic equiparition' principle:

For an ensemble of $N$ $i.i.d$ random variables $X^N=(X_1,X_2....X_N),$ with $N$ sufficiently large, the outcome $x=(x_1,x_2...x_N)$ is almost certain to belong to a subset of $|A_x^N|$ having only $2^{NH(x)}$ members, with each member having probability "close-to" $2^{-NH(x)}$.

And then in the textbook, it also says that typical set doesn't nesscarry to contain the most probable element set.

Because the "smallest-sufficient set" $S_{\delta}$ which defines to be:

the smallest subset of of $A_x$ satisfying $P(x\epsilon S_{\delta})\ge 1-\delta $, for $0\leq{\delta}\leq1. $ In other words, $S_{\delta}$ is constructed by taking the most probable elements in $A_x$, then the second probable......until the total probabily is $\ge1-{\delta}$.

My question is, does the element inside $S_{\delta}$ must be contained in the typical set? If the size of the typical set is identical to the size of $|S_{\delta}|$, then why are we even bother with $S_{\delta}$? Why can't we just take the typical set as our compression scheme instead?

I was following the textbook by David Mackay: Information theory inference and learning algorithms.

I have question on asymptotic equiparition' principle:

For an ensemble of $N$ $i.i.d$ random variables $X^N=(X_1,X_2....X_N),$ with $N$ sufficiently large, the outcome $x=(x_1,x_2...x_N)$ is almost certain to belong to a subset of $|A_x^N|$ having only $2^{NH(x)}$ members, with each member having probability "close-to" $2^{-NH(x)}$.

And then in the textbook, it also says that typical set doesn't nesscarry to contain the most probable element set.

On the other hand, "smallest-sufficient set" $S_{\delta}$ which defines to be:

the smallest subset of of $A_x$ satisfying $P(x\epsilon S_{\delta})\ge 1-\delta $, for $0\leq{\delta}\leq1. $ In other words, $S_{\delta}$ is constructed by taking the most probable elements in $A_x$, then the second probable......until the total probabily is $\ge1-{\delta}$.

My question is, as $N$ increases, does $S_{\delta}$ approaches typical set such that these two sets will end up be equivalent of each other? If the size of the typical set is identical to the size of $|S_{\delta}|$, then why are we even bother with $S_{\delta}$? Why can't we just take the typical set as our compression scheme instead?